Gastos Casa
01 April, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 30/1/2024 | Electricidad | 55759 | Andrés | NA |
| 3/2/2024 | donación | 50000 | Andrés | NA |
| 4/2/2024 | Comida | 46309 | Andrés | NA |
| 5/2/2024 | Comida | 33079 | Tami | Supermercado |
| 8/2/2024 | Enceres | 35440 | Andrés | casaideas |
| 18/2/2024 | VTR | 21990 | Andrés | NA |
| 22/2/2024 | Netflix | 8393 | Tami | NA |
| 25/2/2024 | Enceres | 4973 | Andrés | colgador manguera |
| 25/2/2024 | Enceres | 7980 | Andrés | adaptador vorriente y adaptadores manguera |
| 27/2/2024 | Enceres | 49980 | Andrés | detergente |
| 28/2/2024 | Enceres | 12000 | Andrés | 2 cajas orgamizadoras |
| 28/2/2024 | Electricidad | 56337 | Andrés | PAC ENEL_______ 00000001686518 28/02 |
| 29/2/2024 | Gas | 70997 | Andrés | 68 997 + propina 2 lks |
| 3/3/2024 | Comida | 53553 | Andrés | Supermercado |
| 4/3/2024 | Comida | 6000 | Andrés | pasas |
| 5/3/2024 | Uber cumple papá | 8582 | Tami | NA |
| 6/3/2024 | Agua | 16549 | Andrés | NA |
| 7/3/2024 | Enceres | 4645 | Andrés | descuentos desodorantes |
| 10/3/2024 | Comida | 7470 | Andrés | NA |
| 10/3/2024 | Comida | 90504 | Tami | Supermercado |
| 10/3/2024 | Diosi | 21081 | Andrés | pipeta |
| 11/3/2024 | Diosi | 66970 | Andrés | n&d 50990 + lavanda 3asy clean 10k 79990x2 |
| 17/3/2024 | Comida | 55951 | Tami | Supermercado |
| 19/3/2024 | VTR | 21990 | Andrés | NA |
| 24/3/2024 | Comida | 94384 | Tami | Supermercado |
| 27/3/2024 | Comida | 27980 | Tami | Barras Wild Soul |
| 27/3/2024 | Electricidad | 56338 | Andrés | NA |
| 29/3/2024 | Comida | 69144 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.4857e+08 2 8.0756 3e-04 ***
## lag_depvar 1.0058e+11 1 1914.4053 <2e-16 ***
## Residuals 3.5989e+10 685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 931.1349 13526.54 0.0196518
## 2-0 29308.324 23601.3872 35015.26 0.0000000
## 2-1 22079.486 18730.8790 25428.09 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
## 39 35073.00 1 38443.00
## 40 31422.29 1 35073.00
## 41 30103.29 1 31422.29
## 42 19319.29 1 30103.29
## 43 27926.29 1 19319.29
## 44 30715.43 1 27926.29
## 45 31962.29 1 30715.43
## 46 39790.14 1 31962.29
## 47 39211.57 1 39790.14
## 48 44548.57 1 39211.57
## 49 49398.00 1 44548.57
## 50 41039.00 1 49398.00
## 51 34821.29 1 41039.00
## 52 29123.57 1 34821.29
## 53 21275.71 1 29123.57
## 54 28476.14 1 21275.71
## 55 24561.86 1 28476.14
## 56 20323.57 1 24561.86
## 57 25370.00 1 20323.57
## 58 26811.86 1 25370.00
## 59 27151.86 1 26811.86
## 60 27623.29 1 27151.86
## 61 22896.57 1 27623.29
## 62 41889.29 1 22896.57
## 63 44000.14 1 41889.29
## 64 38558.00 1 44000.14
## 65 43373.86 1 38558.00
## 66 49001.00 1 43373.86
## 67 61213.29 1 49001.00
## 68 58939.57 1 61213.29
## 69 42046.86 1 58939.57
## 70 39191.71 1 42046.86
## 71 42646.43 1 39191.71
## 72 36121.57 1 42646.43
## 73 30915.57 1 36121.57
## 74 20273.43 1 30915.57
## 75 23938.29 1 20273.43
## 76 19274.29 1 23938.29
## 77 21662.29 1 19274.29
## 78 15819.00 1 21662.29
## 79 18126.14 1 15819.00
## 80 17240.71 1 18126.14
## 81 16127.71 1 17240.71
## 82 13917.14 1 16127.71
## 83 15379.86 1 13917.14
## 84 19510.14 1 15379.86
## 85 24567.29 1 19510.14
## 86 25700.43 1 24567.29
## 87 25729.00 1 25700.43
## 88 26435.00 1 25729.00
## 89 31157.14 1 26435.00
## 90 29818.43 1 31157.14
## 91 30962.43 1 29818.43
## 92 28746.71 1 30962.43
## 93 27830.71 1 28746.71
## 94 28252.14 1 27830.71
## 95 28717.57 1 28252.14
## 96 21365.43 1 28717.57
## 97 24816.86 1 21365.43
## 98 16838.57 1 24816.86
## 99 15529.14 1 16838.57
## 100 13286.29 1 15529.14
## 101 13629.43 1 13286.29
## 102 14404.86 1 13629.43
## 103 19524.86 1 14404.86
## 104 18475.71 1 19524.86
## 105 22495.00 1 18475.71
## 106 22254.57 1 22495.00
## 107 24173.29 1 22254.57
## 108 27466.43 1 24173.29
## 109 24602.43 1 27466.43
## 110 20531.14 1 24602.43
## 111 20846.43 1 20531.14
## 112 23875.71 1 20846.43
## 113 36312.71 1 23875.71
## 114 34244.00 1 36312.71
## 115 36347.43 1 34244.00
## 116 39779.71 1 36347.43
## 117 42018.71 1 39779.71
## 118 39372.57 1 42018.71
## 119 33444.00 1 39372.57
## 120 29255.86 1 33444.00
## 121 31640.14 1 29255.86
## 122 29671.14 1 31640.14
## 123 31023.71 1 29671.14
## 124 39723.43 1 31023.71
## 125 39314.14 1 39723.43
## 126 38239.86 1 39314.14
## 127 34649.43 1 38239.86
## 128 36688.43 1 34649.43
## 129 42867.57 1 36688.43
## 130 42226.86 1 42867.57
## 131 32155.14 1 42226.86
## 132 33603.00 1 32155.14
## 133 37254.43 1 33603.00
## 134 33145.57 1 37254.43
## 135 31299.43 1 33145.57
## 136 30252.00 1 31299.43
## 137 26310.71 1 30252.00
## 138 27929.86 1 26310.71
## 139 27666.14 1 27929.86
## 140 25017.57 1 27666.14
## 141 27335.00 1 25017.57
## 142 25760.71 1 27335.00
## 143 18436.86 1 25760.71
## 144 21906.00 1 18436.86
## 145 19418.14 1 21906.00
## 146 22826.14 1 19418.14
## 147 23444.29 1 22826.14
## 148 25264.86 1 23444.29
## 149 25473.29 1 25264.86
## 150 27366.86 1 25473.29
## 151 28855.86 1 27366.86
## 152 32326.86 1 28855.86
## 153 27141.43 1 32326.86
## 154 26297.71 1 27141.43
## 155 23499.14 1 26297.71
## 156 30246.29 1 23499.14
## 157 39931.86 1 30246.29
## 158 38020.43 2 39931.86
## 159 35004.00 2 38020.43
## 160 40750.86 2 35004.00
## 161 42363.29 2 40750.86
## 162 46273.57 2 42363.29
## 163 41083.29 2 46273.57
## 164 35711.29 2 41083.29
## 165 41921.71 2 35711.29
## 166 60583.29 2 41921.71
## 167 63115.57 2 60583.29
## 168 61300.14 2 63115.57
## 169 57666.43 2 61300.14
## 170 55834.00 2 57666.43
## 171 58927.71 2 55834.00
## 172 57810.57 2 58927.71
## 173 48987.14 2 57810.57
## 174 52219.29 2 48987.14
## 175 56503.57 2 52219.29
## 176 56545.00 2 56503.57
## 177 64705.57 2 56545.00
## 178 53833.29 2 64705.57
## 179 50114.00 2 53833.29
## 180 39592.43 2 50114.00
## 181 29907.29 2 39592.43
## 182 33923.29 2 29907.29
## 183 45489.00 2 33923.29
## 184 44866.29 2 45489.00
## 185 51680.57 2 44866.29
## 186 58257.00 2 51680.57
## 187 70600.57 2 58257.00
## 188 76648.00 2 70600.57
## 189 69430.14 2 76648.00
## 190 69651.57 2 69430.14
## 191 77745.14 2 69651.57
## 192 72795.86 2 77745.14
## 193 67670.71 2 72795.86
## 194 55357.86 2 67670.71
## 195 48524.00 2 55357.86
## 196 50154.43 2 48524.00
## 197 45111.57 2 50154.43
## 198 36147.00 2 45111.57
## 199 43501.57 2 36147.00
## 200 41472.43 2 43501.57
## 201 41058.00 2 41472.43
## 202 41605.57 2 41058.00
## 203 49382.86 2 41605.57
## 204 59558.57 2 49382.86
## 205 59134.57 2 59558.57
## 206 61109.00 2 59134.57
## 207 63004.43 2 61109.00
## 208 67344.29 2 63004.43
## 209 78180.86 2 67344.29
## 210 69117.86 2 78180.86
## 211 55597.57 2 69117.86
## 212 49426.14 2 55597.57
## 213 39119.43 2 49426.14
## 214 35636.86 2 39119.43
## 215 39201.14 2 35636.86
## 216 27777.00 2 39201.14
## 217 47207.00 2 27777.00
## 218 55587.29 2 47207.00
## 219 56619.71 2 55587.29
## 220 82679.86 2 56619.71
## 221 91259.57 2 82679.86
## 222 93552.71 2 91259.57
## 223 102242.71 2 93552.71
## 224 91884.00 2 102242.71
## 225 85013.86 2 91884.00
## 226 84535.29 2 85013.86
## 227 80700.43 2 84535.29
## 228 79740.57 2 80700.43
## 229 85163.14 2 79740.57
## 230 86724.86 2 85163.14
## 231 80355.00 2 86724.86
## 232 74875.14 2 80355.00
## 233 81347.00 2 74875.14
## 234 66062.43 2 81347.00
## 235 56946.43 2 66062.43
## 236 47732.14 2 56946.43
## 237 38129.71 2 47732.14
## 238 42928.29 2 38129.71
## 239 45392.57 2 42928.29
## 240 37895.43 2 45392.57
## 241 30660.29 2 37895.43
## 242 42430.86 2 30660.29
## 243 35845.14 2 42430.86
## 244 40350.43 2 35845.14
## 245 31494.71 2 40350.43
## 246 30013.29 2 31494.71
## 247 34197.57 2 30013.29
## 248 37430.14 2 34197.57
## 249 26932.43 2 37430.14
## 250 33729.86 2 26932.43
## 251 38081.43 2 33729.86
## 252 44028.00 2 38081.43
## 253 47139.71 2 44028.00
## 254 46558.86 2 47139.71
## 255 58350.57 2 46558.86
## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 533 51542.59 15336.443
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1939.10790 4006.18619 -504.10272 2467.51978 -2902.45973 543.16121
## 8 9 10 11 12 13
## -5619.95080 -1228.22787 -4010.76594 -505.09519 -5014.39970 -1736.39000
## 14 15 16 17 18 19
## -1025.89800 261.98936 -3331.18797 -493.06289 -2228.68723 6495.55011
## 20 21 22 23 24 25
## -1524.66281 -1218.43247 1457.14364 -1174.86706 235.60158 1706.40459
## 26 27 28 29 30 31
## -7061.10010 891.52269 8164.08900 515.37738 84.19589 -2307.55000
## 32 33 34 35 36 37
## 1631.38167 4650.17108 1266.11455 2536.28306 -1700.56099 4735.60181
## 38 39 40 41 42 43
## 4379.05946 -2148.83506 -2901.71094 -1081.48838 -10731.28991 7148.79297
## 44 45 46 47 48 49
## 2536.83936 1385.33292 8141.02671 831.33806 6665.84741 6926.02864
## 50 51 52 53 54 55
## -5602.95959 -4632.83079 -4983.97795 -7932.41217 6016.33147 -4089.55021
## 56 57 58 59 60 61
## -4961.97022 3728.92930 831.39936 -68.44308 110.62198 -5021.47030
## 62 63 64 65 66 67
## 18035.71110 3814.87187 -3442.38164 6053.13442 7539.15688 14912.70353
## 68 69 70 71 72 73
## 2137.73390 -12799.83007 -1129.05122 4780.77956 -4714.76118 -4310.08415
## 74 75 76 77 78 79
## -10475.62561 2340.33249 -5475.05141 923.48814 -6973.22134 358.52025
## 80 81 82 83 84 85
## -2510.80362 -2862.43006 -4115.94091 -752.37239 2120.13599 3625.67631
## 86 87 88 89 90 91
## 410.21894 -535.59090 145.84073 4260.89934 -1138.35111 1156.79953
## 92 93 94 95 96 97
## -2042.63201 -1053.35547 155.73482 258.78004 -7493.58145 2279.90109
## 98 99 100 101 102 103
## -8666.24285 -3115.20178 -4232.09085 -1960.33151 -1479.96898 2973.24566
## 104 105 106 107 108 109
## -2478.54783 2442.88817 -1253.69485 871.76221 2515.01666 -3180.73284
## 110 111 112 113 114 115
## -4789.28586 -973.13128 1785.04255 11617.18188 -1146.01792 2736.28296
## 116 117 118 119 120 121
## 4359.84580 3647.44837 -923.99424 -4577.16674 -3667.37431 2318.26488
## 122 123 124 125 126 127
## -1700.96500 1344.73515 8881.38314 991.27662 268.93270 -2397.72543
## 128 129 130 131 132 133
## 2728.65789 7154.47953 1200.36541 -8320.40334 1788.04740 4194.47418
## 134 135 136 137 138 139
## -3054.21974 -1367.18633 -827.13013 -3867.73966 1140.48601 -515.51740
## 140 141 142 143 144 145
## -2937.32284 1657.59297 -1909.43266 -7879.57301 1887.30115 -3583.64662
## 146 147 148 149 150 151
## 1963.64358 -348.72789 940.30701 -416.76050 1297.58472 1158.31647
## 152 153 154 155 156 157
## 3348.93624 -4821.17989 -1205.98200 -3279.04967 5874.56433 9758.31686
## 158 159 160 161 162 163
## -3591.71260 -4964.51770 3376.14436 46.89242 2570.66258 -5982.04928
## 164 165 166 167 168 169
## -6890.96048 3938.81168 17260.08091 3745.41758 -247.50500 -2320.14551
## 170 171 172 173 174 175
## -1027.96969 3641.43655 -135.96864 -7998.77422 2820.57046 4325.56012
## 176 177 178 179 180 181
## 682.96268 8807.90998 -9081.59153 -3451.87813 -10775.26292 -11412.98331
## 182 183 184 185 186 187
## 931.20034 9043.58554 -1524.40204 5825.35115 6542.22517 13230.77368
## 188 189 190 191 192 193
## 8664.05528 -3753.94154 2674.06970 10577.23631 -1331.65239 -2200.94059
## 194 195 196 197 198 199
## -10106.72480 -6352.84597 1153.96648 -5290.88429 -9919.13968 5144.00128
## 200 201 202 203 204 205
## -3209.28386 -1878.86730 -974.93177 6331.50127 9819.58434 645.56191
## 206 207 208 209 210 211
## 2984.58499 3182.21684 5892.20879 12996.96874 -5384.31993 -11111.39810
## 212 213 214 215 216 217
## -5656.83168 -10656.77958 -5276.68263 1282.24083 -13206.80529 16046.73176
## 218 219 220 221 222 223
## 7719.30230 1545.58436 26717.94943 12888.73710 7804.24627 14522.38947
## 224 225 226 227 228 229
## -3308.79238 -1271.55244 4157.46182 734.12476 3071.83328 9319.77886
## 230 231 232 233 234 235
## 6218.66356 -1494.10030 -1496.56378 9687.38252 -11162.29163 -7135.20023
## 236 237 238 239 240 241
## -8510.70409 -10189.83563 2865.79403 1203.82320 -8412.34095 -9200.74540
## 242 243 244 245 246 247
## 8791.27256 -7915.87017 2252.42318 -10477.35340 -4343.81790 1114.33742
## 248 249 250 251 252 253
## 748.87211 -12528.50675 3295.82971 1802.34193 4007.02885 2005.32977
## 254 255 256 257 258 259
## -1251.26777 11039.92133 20929.74086 3494.86716 -3980.50985 4300.34280
## 260 261 262 263 264 265
## -1484.42376 3891.02347 -4678.18176 -10811.20563 -4801.38301 -651.24525
## 266 267 268 269 270 271
## -5314.08810 8596.49370 -4336.03633 4079.36875 -2158.41906 4351.50961
## 272 273 274 275 276 277
## 686.70634 7283.61127 -1347.71819 12053.85122 -4422.04638 1798.44456
## 278 279 280 281 282 283
## -298.15536 7900.91900 -4928.87275 -2690.97425 -11268.36202 -2819.57673
## 284 285 286 287 288 289
## 18484.71492 7852.77724 2878.00221 -481.18464 1016.06674 6494.40893
## 290 291 292 293 294 295
## 7032.24322 -18570.47364 -11195.04209 -8307.80356 9402.51797 2953.76288
## 296 297 298 299 300 301
## -1251.58465 27318.73509 10307.16138 5219.14021 9841.93651 3239.56019
## 302 303 304 305 306 307
## -676.21822 8184.28278 -23962.26282 -3534.10597 -220.33664 -7013.69597
## 308 309 310 311 312 313
## -4095.51586 2776.20709 -9296.44174 -3428.09941 -8395.77066 1289.83849
## 314 315 316 317 318 319
## -3372.60523 1819.23707 -4259.53803 27242.90869 -611.05828 3372.55369
## 320 321 322 323 324 325
## 10930.09597 5790.02124 32609.77161 5669.80437 -20401.25204 2014.11633
## 326 327 328 329 330 331
## 1317.02296 -6279.15310 -1651.12452 -33219.53514 583.04220 -2543.66771
## 332 333 334 335 336 337
## -320.60230 -3358.80305 3891.76169 -553.71851 -7052.77783 -3274.00157
## 338 339 340 341 342 343
## -2355.71235 -7838.96822 3636.11395 -1509.58633 -1866.75627 -1118.59024
## 344 345 346 347 348 349
## 63.73448 391.45956 -1686.31665 -9518.43287 -13370.87662 2032.03910
## 350 351 352 353 354 355
## -4532.88726 -3880.99574 -6206.03181 1496.38970 1188.39584 2602.53699
## 356 357 358 359 360 361
## -3864.41188 -639.01719 568.40333 6929.10028 279.78008 -31.34086
## 362 363 364 365 366 367
## 2589.86105 -2715.64782 -874.27694 -8746.93659 -4716.13142 -6333.16432
## 368 369 370 371 372 373
## -5112.76248 -7439.03533 4784.39473 228.43277 7004.23368 -7659.01772
## 374 375 376 377 378 379
## -2357.75534 -3487.99467 -2583.02291 -12577.38887 1675.88939 -10804.48296
## 380 381 382 383 384 385
## 5444.36928 9182.37316 3092.39668 -2393.88823 1585.75111 6745.76719
## 386 387 388 389 390 391
## 11481.41122 -5627.22366 -5277.20039 -142.43987 8572.47538 1907.36754
## 392 393 394 395 396 397
## 11315.74040 -9688.89480 2828.90988 782.93663 625.09688 -598.97223
## 398 399 400 401 402 403
## -527.22952 -14466.43307 8396.63759 -1199.16495 -1398.63058 6946.99101
## 404 405 406 407 408 409
## -7891.44556 -1338.99016 -2576.47478 -5877.86032 -2961.19130 -4026.59207
## 410 411 412 413 414 415
## -8881.55846 5944.62467 1544.78029 -7434.86026 -7813.23182 14046.53628
## 416 417 418 419 420 421
## 3808.53731 4521.92387 -7967.13920 -4762.88139 -2659.30730 2749.75864
## 422 423 424 425 426 427
## -14038.28653 -2948.19764 -9254.05947 2796.66674 6827.37622 6519.77342
## 428 429 430 431 432 433
## -3967.63712 -4139.29158 -4773.73736 -1874.91506 -5796.99165 -6750.49573
## 434 435 436 437 438 439
## -6115.58435 -1588.50982 -1022.85908 -5127.66049 2407.29441 4721.07786
## 440 441 442 443 444 445
## -5105.45702 -2245.07023 1486.05937 -3893.44563 2754.63757 -6613.90095
## 446 447 448 449 450 451
## -12201.12194 -4704.19778 9441.48227 -2102.79983 4680.12105 -5878.60764
## 452 453 454 455 456 457
## -1182.54368 328.39641 2989.74404 -12262.30780 3256.87411 -6756.18737
## 458 459 460 461 462 463
## 6414.15901 2992.90305 2527.78115 -3795.89695 2103.25561 29.70314
## 464 465 466 467 468 469
## 1831.31361 -463.61465 3400.94650 -2555.46333 5857.15134 -6831.23962
## 470 471 472 473 474 475
## -2931.30754 -2198.07244 -4670.70325 2952.29067 7798.75206 -5928.17117
## 476 477 478 479 480 481
## 1507.80140 -6134.58081 -2860.42093 1978.43709 -12928.88920 -9878.93238
## 482 483 484 485 486 487
## -1400.90897 -153.93649 -1102.83381 -1463.85079 -9694.64643 10909.94385
## 488 489 490 491 492 493
## 6199.72302 7462.69542 -5315.33700 5423.55623 9404.89276 6253.57617
## 494 495 496 497 498 499
## -13232.09565 -10489.21816 -3475.04697 -1160.82957 -573.27559 -7663.66412
## 500 501 502 503 504 505
## 509.89580 4217.52761 5503.00169 722.44245 150.41248 -7168.42536
## 506 507 508 509 510 511
## 565.40101 -5036.10297 1800.75467 -1294.42972 -8159.47935 -678.43128
## 512 513 514 515 516 517
## -2736.15520 -658.45652 1275.45072 -9519.96214 -7875.93305 24119.76423
## 518 519 520 521 522 523
## 9945.09016 6093.58402 -5081.93584 2969.11087 17203.24112 11816.43278
## 524 525 526 527 528 529
## -23728.12166 -4941.93866 -3676.32023 4590.75943 -285.02554 -11036.77760
## 530 531 532 533 534 535
## 4341.02231 13920.67548 -4814.66937 4468.51761 5689.04638 -1610.62949
## 536 537 538 539 540 541
## -4397.94297 -6990.54479 -2093.57642 8315.83847 216.89343 -8054.56580
## 542 543 544 545 546 547
## 1814.83460 -572.51226 392.40073 -10995.45690 -11136.71410 1870.51733
## 548 549 550 551 552 553
## 6888.19637 -1334.10948 818.61129 -7718.06488 8496.44503 946.76642
## 554 555 556 557 558 559
## -11891.07453 9092.44715 8701.59625 234.57921 4978.29263 -3408.80830
## 560 561 562 563 564 565
## 14221.00606 21748.86207 -6022.71371 -9360.60230 6956.23577 458.89575
## 566 567 568 569 570 571
## 3663.63679 -7154.89460 -17185.41866 6598.71752 6459.55270 2000.67805
## 572 573 574 575 576 577
## 3212.38333 1909.99256 -2016.51367 14831.73621 -9388.59092 -6113.71051
## 578 579 580 581 582 583
## 8767.23275 3006.16076 -6380.04307 7591.73722 -3641.17304 -2669.71644
## 584 585 586 587 588 589
## 15774.11768 -14264.80404 8486.37183 217.88537 -6075.25754 -691.00580
## 590 591 592 593 594 595
## 308.97742 -10590.89153 1749.88522 -7154.67410 2995.32300 8847.73309
## 596 597 598 599 600 601
## -7412.73049 5858.95608 2813.53290 6962.61925 -3015.83869 6276.54084
## 602 603 604 605 606 607
## -8113.81645 2337.88791 1375.99026 3254.07705 1639.13770 548.35107
## 608 609 610 611 612 613
## -5665.01962 8150.39089 -1021.57314 -2434.99714 -3348.28798 -8161.59257
## 614 615 616 617 618 619
## 11941.88591 5061.71402 -9141.64562 11688.92953 6240.28722 -5331.78445
## 620 621 622 623 624 625
## 26521.62732 -12389.05673 -6525.46983 3318.10512 -3972.79172 -10462.34819
## 626 627 628 629 630 631
## 11308.42183 -21489.67153 -2510.05697 8580.81158 11162.16115 -1394.97983
## 632 633 634 635 636 637
## 33417.25713 -6096.68834 6082.87765 5790.84797 -1857.83664 -5001.23583
## 638 639 640 641 642 643
## -1687.25584 -12219.92640 -2181.95489 -1844.65531 -2491.00289 -2846.14360
## 644 645 646 647 648 649
## 1808.20656 4458.96392 17051.53108 18826.93567 1335.38740 5201.58549
## 650 651 652 653 654 655
## 11033.25078 20644.23769 1409.99766 -27463.50328 -1115.89785 -2094.01941
## 656 657 658 659 660 661
## 2031.07792 -3012.28685 -10483.81829 1682.80983 4278.50841 -896.40981
## 662 663 664 665 666 667
## 13133.88811 1613.08538 2064.89893 -11435.52265 1484.61362 1312.23204
## 668 669 670 671 672 673
## -5026.13715 -7329.44397 2070.71085 -3665.39352 2689.63440 -3315.42303
## 674 675 676 677 678 679
## -9302.04196 -8366.92456 -3111.41500 37.89898 2748.79663 678.45215
## 680 681 682 683 684 685
## -3830.86735 -1839.62674 -1349.12956 -8267.29729 4547.16493 -2258.41192
## 686 687 688 689 690
## -1421.15651 568.55454 10861.74250 10002.95486 10892.23080
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17330.18 20132.81 24320.25 24042.62 26359.17 23733.55 24438.67 19745.37
## 10 11 12 13 14 15 16 17
## 19486.05 16870.38 17635.69 14416.25 14466.61 15120.87 16790.90 15137.21
## 18 19 20 21 22 23 24 25
## 16155.69 15539.02 22510.66 21609.00 21097.00 22957.44 22293.97 22936.31
## 26 27 28 29 30 31 32 33
## 24753.39 18776.76 20475.91 28190.62 28247.38 27925.41 25591.90 26972.40
## 34 35 36 37 38 39 40 41
## 30755.31 31098.29 32485.42 30034.97 34063.94 37221.84 34324.00 31184.77
## 42 43 44 45 46 47 48 49
## 30050.58 20777.49 28178.59 30576.95 31649.12 38380.23 37882.72 42471.97
## 50 51 52 53 54 55 56 57
## 46641.96 39454.12 34107.55 29208.13 22459.81 28651.41 25285.54 21641.07
## 58 59 60 61 62 63 64 65
## 25980.46 27220.30 27512.66 27918.04 23853.57 40185.27 42000.38 37320.72
## 66 67 68 69 70 71 72 73
## 41461.84 46300.58 56801.84 54846.69 40320.77 37865.65 40836.33 35225.66
## 74 75 76 77 78 79 80 81
## 30749.05 21597.95 24749.34 20738.80 22792.22 17767.62 19751.52 18990.14
## 82 83 84 85 86 87 88 89
## 18033.08 16132.23 17390.01 20941.61 25290.21 26264.59 26289.16 26896.24
## 90 91 92 93 94 95 96 97
## 30956.78 29805.63 30789.35 28884.07 28096.41 28458.79 28859.01 22536.96
## 98 99 100 101 102 103 104 105
## 25504.81 18644.34 17518.38 15589.76 15884.83 16551.61 20954.26 20052.11
## 106 107 108 109 110 111 112 113
## 23508.27 23301.52 24951.41 27783.16 25320.43 21819.56 22090.67 24695.53
## 114 115 116 117 118 119 120 121
## 35390.02 33611.15 35419.87 38371.27 40296.57 38021.17 32923.23 29321.88
## 122 123 124 125 126 127 128 129
## 31372.11 29678.98 30842.05 38322.87 37970.92 37047.15 33959.77 35713.09
## 130 131 132 133 134 135 136 137
## 41026.49 40475.55 31814.95 33059.95 36199.79 32666.61 31079.13 30178.45
## 138 139 140 141 142 143 144 145
## 26789.37 28181.66 27954.89 25677.41 27670.15 26316.43 20018.70 23001.79
## 146 147 148 149 150 151 152 153
## 20862.50 23793.01 24324.55 25890.05 26069.27 27697.54 28977.92 31962.61
## 154 155 156 157 158 159 160 161
## 27503.70 26778.19 24371.72 30173.54 41612.14 39968.52 37374.71 42316.39
## 162 163 164 165 166 167 168 169
## 43702.91 47065.33 42602.25 37982.90 43323.20 59370.15 61547.65 59986.57
## 170 171 172 173 174 175 176 177
## 56861.97 55286.28 57946.54 56985.92 49398.72 52178.01 55862.04 55897.66
## 178 179 180 181 182 183 184 185
## 62914.88 53565.88 50367.69 41320.27 32992.09 36445.41 46390.69 45855.22
## 186 187 188 189 190 191 192 193
## 51714.77 57369.80 67983.94 73184.08 66977.50 67167.91 74127.51 69871.65
## 194 195 196 197 198 199 200 201
## 65464.58 54876.85 49000.46 50402.46 46066.14 38357.57 44681.71 42936.87
## 202 203 204 205 206 207 208 209
## 42580.50 43051.36 49738.99 58489.01 58124.42 59822.21 61452.08 65183.89
## 210 211 212 213 214 215 216 217
## 74502.18 66708.97 55082.97 49776.21 40913.54 37918.90 40983.81 31160.27
## 218 219 220 221 222 223 224 225
## 47867.98 55074.13 55961.91 78370.83 85748.47 87720.32 95192.79 86285.41
## 226 227 228 229 230 231 232 233
## 80377.82 79966.30 76668.74 75843.36 80506.19 81849.10 76371.71 71659.62
## 234 235 236 237 238 239 240 241
## 77224.72 64081.63 56242.85 48319.55 40062.49 44188.75 46307.77 39861.03
## 242 243 244 245 246 247 248 249
## 33639.58 43761.01 38098.01 41972.07 34357.10 33083.23 36681.27 39460.94
## 250 251 252 253 254 255 256 257
## 30434.03 36279.09 40020.97 45134.38 47810.12 47310.65 57450.26 74673.42
## 258 259 260 261 262 263 264 265
## 74491.37 67906.80 69365.42 65645.41 67068.90 60924.35 50366.95 46456.53
## 266 267 268 269 270 271 272 273
## 46662.66 42830.36 51496.61 47828.06 51909.85 50055.92 54059.58 54350.96
## 274 275 276 277 278 279 280 281
## 60274.15 57945.43 67466.90 61486.84 61693.58 60068.51 65721.44 59550.12
## 282 283 284 285 286 287 288 289
## 56167.79 45883.72 44305.57 61267.94 66711.43 67114.47 64572.50 63674.16
## 290 291 292 293 294 295 296 297
## 67612.47 71461.47 52755.61 43012.66 37117.48 47277.24 50468.30 49596.12
## 298 299 300 301 302 303 304 305
## 73413.55 79265.86 79923.06 84463.30 82690.08 77798.15 81210.69 56502.53
## 306 307 308 309 310 311 312 313
## 52822.19 52506.98 46394.37 43647.51 47194.44 39863.24 38605.34 33252.02
## 314 315 316 317 318 319 320 321
## 36977.32 36171.48 39942.97 37958.95 63341.63 61216.59 62814.76 70687.69
## 322 323 324 325 326 327 328 329
## 73037.66 98120.48 96523.54 72732.03 71548.69 69931.72 62009.41 59176.68
## 330 331 332 333 334 335 336 337
## 29595.39 33225.24 33657.89 35941.52 35292.67 40969.43 42028.21 37350.14
## 338 339 340 341 342 343 344 345
## 36576.86 36701.54 32093.74 37998.87 38651.90 38906.30 39768.41 41526.40
## 346 347 348 349 350 351 352 353
## 43319.89 43075.43 36130.45 26845.82 32106.89 30985.71 30582.17 28235.90
## 354 355 356 357 358 359 360 361
## 32841.60 36537.18 40930.98 39148.30 40388.88 42493.90 49773.51 50315.48
## 362 363 364 365 366 367 368 369
## 50514.00 52938.65 50461.42 49914.65 42674.85 39915.45 36152.19 33965.61
## 370 371 372 373 374 375 376 377
## 30085.03 37259.00 39510.19 47272.45 41338.33 40794.14 39354.31 38894.39
## 378 379 380 381 382 383 384 385
## 29904.82 34431.05 27591.35 35682.20 45853.75 49363.46 47663.82 49624.38
## 386 387 388 389 390 391 392 393
## 55747.30 65084.51 58401.91 52956.58 52689.52 59953.78 60468.97 69002.18
## 394 395 396 397 398 399 400 401
## 58278.09 59820.49 59387.47 58879.40 57389.94 56170.86 43136.36 51587.88
## 402 403 404 405 406 407 408 409
## 50603.92 49586.29 55887.59 48546.56 47868.47 46221.29 41966.05 40815.02
## 410 411 412 413 414 415 416 417
## 38909.13 33095.52 40845.36 43726.00 38481.52 33646.46 48285.89 52070.65
## 418 419 420 421 422 423 424 425
## 55938.57 48525.31 44906.02 43602.67 47133.14 35733.05 35466.49 29814.90
## 426 427 428 429 430 431 432 433
## 35317.48 43515.08 50299.64 47115.58 44230.02 41203.20 41093.13 37625.92
## 434 435 436 437 438 439 440 441
## 33824.58 31101.80 32653.29 34473.80 32509.56 37299.78 43408.46 40211.50
## 442 443 444 445 446 447 448 449
## 39922.08 42881.59 40800.65 44727.90 40048.98 31221.20 30076.80 41256.51
## 450 451 452 453 454 455 456 457
## 40943.02 46506.04 42210.26 42554.46 44149.68 47809.88 37842.13 42615.76
## 458 459 460 461 462 463 464 465
## 38110.41 45561.38 49026.50 51606.18 48386.74 50691.01 50889.40 52609.19
## 466 467 468 469 470 471 472 473
## 52114.62 55012.46 52382.42 57354.81 50719.88 48368.07 46976.27 43653.28
## 474 475 476 477 478 479 480 481
## 47350.82 54697.74 49211.63 50888.30 45758.42 44162.71 46951.46 36530.79
## 482 483 484 485 486 487 488 489
## 30192.77 32032.94 34687.55 36154.28 37105.08 30845.06 43179.85 49736.16
## 490 491 492 493 494 495 496 497
## 56459.91 51253.87 56011.54 63526.14 67278.10 53748.79 44473.62 42529.40
## 498 499 500 501 502 503 504 505
## 42847.56 43626.38 38199.10 40560.62 45779.43 51372.41 52071.02 52179.85
## 506 507 508 509 510 511 512 513
## 45980.03 47299.10 43616.67 46329.14 46000.05 39813.86 40927.30 40115.31
## 514 515 516 517 518 519 520 521
## 41203.69 43802.53 36754.36 32107.38 55624.34 63657.70 67253.65 60736.03
## 522 523 524 525 526 527 528 529
## 62054.62 75428.28 82296.12 57637.22 52587.32 49333.24 53643.88 53157.92
## 530 531 532 533 534 535 536 537
## 43494.69 48408.61 60871.53 55477.91 58822.53 62748.06 59846.66 54954.97
## 538 539 540 541 542 543 544 545
## 48519.29 47196.16 55009.39 54763.71 47439.88 49628.80 49458.17 50141.17
## 546 547 548 549 550 551 552 553
## 40936.14 32899.34 37173.38 45163.25 44963.39 46642.64 40745.98 49618.23
## 554 555 556 557 558 559 560 561
## 50755.50 40694.27 50086.26 57826.28 57201.14 60742.67 56575.99 68152.85
## 562 563 564 565 566 567 568 569
## 84580.86 74826.60 63568.76 67918.96 66072.65 67240.75 58942.42 43181.57
## 570 571 572 573 574 575 576 577
## 50080.73 55893.61 57057.90 59101.01 59737.94 56909.26 68964.59 58504.00
## 578 579 580 581 582 583 584 585
## 52325.05 59807.84 61288.33 54490.26 60658.89 56304.15 53394.88 66752.95
## 586 587 588 589 590 591 592 593
## 52409.20 59638.69 58745.26 52565.58 51881.59 52153.32 43014.26 45767.39
## 594 595 596 597 598 599 600 601
## 40477.82 44657.27 53283.59 46719.04 52486.47 54827.10 60407.55 56625.74
## 602 603 604 605 606 607 608 609
## 61364.25 53064.68 54915.30 55679.49 57951.58 58516.65 58064.59 52333.04
## 610 611 612 613 614 615 616 617
## 59284.29 57374.71 54517.29 51274.88 44347.83 55678.14 59504.79 50581.93
## 618 619 620 621 622 623 624 625
## 60821.28 64940.78 58532.37 80412.34 65767.76 58217.04 60188.65 55614.63
## 626 627 628 629 630 631 632 633
## 46101.15 56641.10 37501.49 37363.90 46782.55 57101.27 55176.46 83456.12
## 634 635 636 637 638 639 640 641
## 73795.84 75962.15 77573.84 72382.66 65215.83 61902.78 49996.95 48390.80
## 642 643 644 645 646 647 648 649
## 47299.72 45805.72 44215.65 46850.61 51395.75 66132.35 80330.90 77499.27
## 650 651 652 653 654 655 656 657
## 78388.89 84168.48 97402.72 92243.36 62978.75 60470.45 57472.49 58441.72
## 658 659 660 661 662 663 664 665
## 54938.39 45501.19 47848.21 52098.41 51303.25 62684.06 62563.67 62848.67
## 666 667 668 669 670 671 672 673
## 51484.81 52823.05 53825.57 49237.30 43311.29 46298.68 43935.08 47367.28
## 674 675 676 677 678 679 680 681
## 45154.90 38104.64 32846.27 32843.82 35549.77 40207.69 42432.72 40468.48
## 682 683 684 685 686 687 688 689
## 40491.70 40933.44 35364.41 41594.70 41100.01 41394.59 43358.83 53898.90
## 690
## 62223.77
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8246
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 8.075634 0.5060371 3.504197
## t2* 1914.405267 23.6052133 226.306327
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.491382 8.211244 14.8626
## 2 lag_depvar 1585.695012 1926.351453 2329.9042Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Apr 01 00:42:04 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 01 00:42:11 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Apr 01 00:42:18 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Apr 01 00:42:25 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Apr 01 00:42:32 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 01 00:42:39 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 01 00:42:46 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Apr 01 00:42:53 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Apr 01 00:43:00 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Apr 01 00:43:07 2024
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 5.516333 | 5.195333 | 5.410333 | 5.849167 | 6.5345098 |
| Comida | 288.100000 | 366.009167 | 310.278417 | 317.896583 | 343.3277255 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 74.633333 | 38.104750 | 47.072333 | 29.523000 | 36.2265882 |
| Enceres | 38.339333 | 18.259750 | 20.086417 | 14.801167 | 23.9398039 |
| Farmacia | 0.000000 | 4.733250 | 1.831667 | 13.996083 | 8.1406471 |
| Gas/Bencina | 23.665667 | 35.219333 | 44.325000 | 13.583667 | 27.3653529 |
| Diosi | 29.350333 | 55.804250 | 31.180667 | 52.687833 | 43.3250980 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 14.340167 | 5.3866471 |
| Electrodomésticos/ Mantención casa | 20.000000 | 0.000000 | 3.944000 | 56.595000 | 17.4405490 |
| VTR | 21.990000 | 12.829167 | 25.156667 | 19.086917 | 19.2197647 |
| Netflix | 5.565667 | 4.555500 | 7.151583 | 7.028750 | 6.6763529 |
| Otros | 0.000000 | 0.000000 | 3.151083 | 0.000000 | 0.7414314 |
| Total | 507.160667 | 540.710500 | 499.588167 | 545.388333 | 538.3244706 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2291, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-04-09 00:04:58 sería de: 37.761 pesos// Percentil 95% más alto proyectado: 40.872,77
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37183.29 | 37180.57 |
| Lo.80 | 37214.14 | 37215.55 |
| Point.Forecast | 37760.78 | 39294.66 |
| Hi.80 | 39520.43 | 44169.68 |
| Hi.95 | 40484.89 | 46750.36 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2841 1016.1292
## s.e. 0.1275 29.2255
##
## sigma^2 = 27810: log likelihood = -397.69
## AIC=801.38 AICc=801.81 BIC=807.72paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2520 697.6354 10.1356
## s.e. 0.1296 268.0070 8.4706
##
## sigma^2 = 27678: log likelihood = -397.02
## AIC=802.03 AICc=802.75 BIC=810.47forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 771.1511 | 675.2312 | 745.5439 |
| Lo.80 | 887.7787 | 793.2280 | 832.9201 |
| Point.Forecast | 1108.0936 | 1016.1291 | 1026.8549 |
| Hi.80 | 1328.4084 | 1239.0303 | 1265.8909 |
| Hi.95 | 1445.0360 | 1357.0271 | 1414.1699 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))